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Record W2955326289 · doi:10.22215/etd/2015-10949

Three Essays on the Labour Market Implications of Youth Training Programs and Institutional Changes in Professional Sports

2015· dissertation· en· W2955326289 on OpenAlexaff
Mihailo Radoman

Bibliographic record

Venuenot available
Typedissertation
Languageen
FieldEconomics, Econometrics and Finance
TopicSports Analytics and Performance
Canadian institutionsCarleton University
Fundersnot available
KeywordsSpellLeagueEndogeneityRegression discontinuity designDemographic economicsTest (biology)Promotion (chess)Labour economicsPolitical scienceEconomicsAccountingMarketingBusinessEconometricsSociologyStatisticsMathematicsLaw

Abstract

fetched live from OpenAlex

The firs chapter of the thesis examines the impact of youth training programs on career and spell duration of professional athletes.A unique data set of post-war English trained soccer players is used to study the impact of the youth training program they attended on their career and spell duration.The results indicate that the duration patterns of players are dependent on the youth academy they attended.The spell analysis outlines the nature of the competitive environment between smaller and larger clubs.Finally, the results of career and spell duration analyses addressed unobserved heterogeneity, allowed for nonlinearity of covariates using the cubic spline methodology, and were tested for endogeneity bias using a split sample test.The second chapter analyzes certain labour market implications of institutional changes in professional sports.The study examines the impact of the Bosman ruling on the competitive nature of new entrants to the English Premier League.Relevant labour economics literature would predict that post-Bosman entrants will be more productive and consequently have a higher probability of securing a first-tea spot in top European leagues.To test these predictions, proprietary data was collected on all players that entered the English Premier League in four-year windows around the Bosman ruling.Regression Discontinuity Design displayed evident discontinuity in certain have to start with my supervisor, Dr. Marcel Voia, without whom this project would be only a distant dream.His timely motivation and guidance throughout the process are greatly appreciated, and I owe a large debt to him in giving me invaluable educational and personal advice at times when I stumbled and questioned myself on this path.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.006
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.008
Threshold uncertainty score0.028

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.002
Science and technology studies0.0020.002
Scholarly communication0.0030.002
Open science0.0010.002
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0080.001

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.082
GPT teacher head0.271
Teacher spread0.188 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations0
Published2015
Admission routes1
Has abstractyes

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